Driving forces of the Canadian economy: An
Bibliographic record
Abstract
This paper analyses the Canadian economy for the post 1960 period. It uses an accounting procedure developed in Chari, Kehoe, and McGrattan (2006). The procedure identifies accounting factors that help align the predictions of the neoclassical growth model with macroeconomic variables observed in the data. The paper finds that the total factor productivity (the productivity factor) and the consumption-leisure trade-off (the labor factor) are key to understand the changes in output, labor supply and labor productivity observed in the Canadian economy. The paper performs a decomposition of the labor factor for Canada and the United States. It finds that the decline in the gender-wage gap is a major driving force of the decrease in the labor market distortions. Moreover, the milder reduction in the labor market distortions observed in Canada, compared to the US, is due to a relative increase in effective labor taxes in Canada. We are grateful to Ellen R. McGrattan for advice, we thank Malik Shukayev and Richard Dion for comments
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".